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The past few years have seen exciting and inspirational ideas emerging from a variety of sources, all focused on how to make the next generation of Americans productive, happy, efficient, insightful, innovative. In short, the next great generation of world leaders. I’ve read a number of books that talk about education writ large, meaning K-12 and higher ed, and in fact life long learning. The similarities of these books with each other are fairly striking, and they are largely getting at many of the same topics from different angles. But there are differences too. Let’s look at some of the recent books on my bookshelf and see what advice they give us.

The Global Achievement Gap, by Tony Wagner (2008). Tony Wagner is co-Director of the Change Leadership Group in the Graduate School of Education at Harvard and has been a leading voice in education policy and practice for quite some time. Despite the ominous subtitle (“Why even our best schools don’t teach the new survival skills our children needs–and what we can do about it”), the book presents a compelling prescription for what the problem is. Wagner’s “7 Survival Skills for Teens Today” hit on the key habits of mind that emergent adults need to thrive in the modern economy. The 7 survival skills are:

critical thinking and problem solving

collaboration across networks and leading by influence

agility and adaptability

initiative and entrepreneurialism

effective oral and written communication

accessing and analyzing information

curiosity and imagination

I don’t think any of these is really a modern skill, one brought on the by the information age, with the possible exception of collaboration across networks (which is often mediated by technology). I think of these as time-tested skills that had assumed new urgency and importance in today’s uber-competitive world.

A Whole New Mind, by Daniel Pink (2005). This book’s subtitle also jabs at the notion of quantitative thinking as the key to mastering the modern world. “Why right-brainers will rule the future” proclaims the small print on the cover, as it unveils a series of ideas about how the right side of the brain holds the key to unlock the future’s piggy bank of health and wealth. Pink’s ideas about “high concept, high touch” enterprises (“1. can someone overseas do it cheaper? 2. can a computer do it faster? 3. Is what I’m offering in demand in the age of abundance?”) are certainly the right questions, and I actually like this book a lot. Pink also present six “senses” for the modern age, which are:

not just function, but also design

not just argument but also story

not just focus but also symphony

not just logic but also empathy

not just seriousness but also play

not just accumulation but also meaning

What I like about this framing is that it starts with actions on the low end of the cognitive taxonomy (gathering, knowing, understanding; more on this later…) and ends with the high-end cognitive skills (designing, creating, composing, etc.).

How Children Succeed, by Paul Tough (2012). One of my favorite books in the category, this one provides perhaps the most progressive view of how we engage students in authentic learning while simultaneously building character. This is also the most thoroughly modern book, in the sense that it provides a very accessible review of the currently-hot literature on “grit” and similar non-cognitive factors in success. The idea is that the traditional cognitive skills (math, reading, and so on) we teach in schools are important, but a more critical task is to cultivate students who possess this non-cognitive strength, can overcome challenges, are resilient in the face of failure, and have that elusive “it” that makes them driven to succeed. A vast simplification of a subtle and interesting book would focus on the three qualities mentioned in its subtitle:

This book encapsulates what I believe to be the best thinking about success and failure, and the role that these non-cognitive skills play. I’ve written about this recently, using data from my own institution to make the point that the input credentials of our students are fairly uniform and very strong. But once they arrive in my school, their ability to succeed changes.

But what, you say, about the specifics of engineering education. Glad you asked.

The Engineer of 2020, the National Academy of Engineering (2004). This book in some ways generated the long line of urgent calls for reform in engineering education, including publications like the Gathering Storm report or the Duderstadt report. The question it works to address is: what are the competencies engineers will need in the world of 2020 and beyond? The answer will not surprise you. In addition to the basic literacies in mathematics and science, plus discipline-specific expertise, the engineer of 2020 needs:

strong analytical skills

practical ingenuity

creativity

communication skills

business fundamentals

leadership skills

high ethical standards and professionalism

dynamism, agility, resilience, and flexibility

the passion to be a lifelong learner

It is rather striking the entirety of the “traditional” engineering curriculum (math, science, and discipline-specific knowledge) is lumped into a single entry on this list! Okay, so it’s number 1 on the list. But still, it’s only one of many important entries.

In part 2 of this, I’m going to engage in some analysis of this information and frame it in the context of learning taxonomies. Yes, sounds geeky. But it’s a useful way to synthesize all this into a more concrete understanding. Stay tuned.

Yes, I am. I’m this 90%. And this one. And this one too. (But I hope not in this one.) But I’m also in this one: the 90% of MOOC students who do not finish the course. And I’m okay with that. On balance, my MOOC experience has been quite positive: my first course in Fall 2012 was “Computing for Data Analysis”, taught by Roger Peng from Johns Hopkins, and it was a well-constructed and nicely-delivered course. Not shift-the-Earth-off-its-axis great, but very serviceable and for self-motivated learners it served a nice purpose. I am currently enrolled in “Data Analysis”, offered by Jeff Leek also from Johns Hopkins. It’s also a well-thought-out course that (so far) has given a gentle but useful overview of doing data analysis, especially on large datasets. Great.

But think of the pedagogical challenges associated with developing a MOOC. Your students, perhaps over 100,000 of them, are:

from dozens or more different countries around the world, with different cultural views and experiences of education

from all age groups and levels of previous academic achievement

equipped with different levels of academic ambition (i.e., some want to take the course for very specific career-related reasons, some might want to simply “sit in” and observe)

confronted with different levels of constraints upon their time available to devote to the course

and so on…

Essentially, when designing a MOOC you are trying to develop an educational experience that respects and reflects all those differences listed above (and more), yet serve some segment of the student population–presumably you try to teach to the students that will finish the course–whose thirst for the course content is highest. Moreover, you are probably modeling the course after an existing, in-person course offered at a brick-and-mortar institution somewhere, and “translating” it to the MOOC domain.

So back to why I am the 90%. I work, have kids, engage in the community, and do all the things that lots of other people do with their time. I am interested in the subject matter of my two courses, and it’s certainly helpful for my job, but I’d hesitate to say that it makes a discrete difference in the quality or quantity of my work. So I’m not motivated enough to actually complete all the work in the course. One night, I say down in bed at 10 pm to do one of the programming assignments for the Computing for Data Analysis course, and it was literally about 5 am when I realized what had happened. I was immersed in the material, it was interesting, I was definitely learning about the R programming language, but this was no way to live. It took me two days to recover from doing my homework.

So I am happily part of the 90%. I learned the course material to a large degree, I can perform lots of basic functions in R and am using R right now to analyze some large-ish-scale data we collected from our students (about 1000 rows and about 40 columns). So I continue to get smarter even though the course is over. And it’s helpful that the course I’m in now (that I’m not planning to complete) also uses R as the computing platform. But I am fully happy to be 2-for-2 (or is it 0-for-2?) in MOOCs. And perhaps for this segment of the population (i.e., working professionals interested in the subject matter), this is the best we can hope for. I still wonder about the students for whom the course subject matter and skills would make a discrete difference in their life and/or employment prospects…how do we get them out of the 90%? Is it possible that if the course really would make a discrete difference, they would be self-motivated enough to not end up in the 90%?

To be sure, not all MOOCs are created the same. Many are excellent, some are okay, and a few are not so good at all. But this is an experiment worth doing, despite people like me in the 90%. What we learn about delivering course content via the MOOC platform could add a lot of value to how we teach face-to-face. The content-instruction-assessment triad of teaching and learning takes on new importance in the MOOC, and some very thoughtful people are working, right now, on compelling MOOC pedagogies.

But what can the 90% learn about teaching by taking a MOOC? That’s a better question, and here’s what I think. My first three observations so far are, you are saying, the obvious things that any conscientious teacher will do for his or her class (and you’d be right):

to think very carefully about the preparation of students in the class, and more specifically the variation in preparations especially in a large class

to consider how students can access help via the instructors and TAs, especially online and asynchronously

to develop assessments that are sensible and try to measure the things that are important

And of course we should follow some basic best practices in how to present materials, use hand-written or PPT notes, etc. The really enlightening thing for is this: the social constructivist part of this, including peer support, peer review for grading, and essentially group construction of knowledge and meaning around the course material is exceptionally powerful in a MOOC. This notion (i) turns that variation in preparation into an asset by enlisted more prepared students to help and support the less prepared students (both formally and informally), and (ii) the peer review part of it absolutely falls into the category of “sensible” (i.e., scalable) and, if you are careful and deliberate in planning your exercises and assessments, will measure the right things in a meaningful way.

This is powerful, for sure. Where MOOCs fall down as an educational endeavor might be there relative lack of interactivity and the all-important active learning strategies that we talk about so much in educational circles there days. I think we shouldn’t be too hard on MOOCs in this regard, because on any college campus, on any given day, in any discipline, I bet we can find a face-to-face class with the most dismal, non-active, disengaging lecture approach that has ever existed since the dawn of time. So, let’s not hold face-t0-face instruction as the gold standard here, because the abuses of face-to-face class time are many, honed by years and years of dedicated practice (ha), and so saddled by instructor inertia as to be virtually unsolvable.

But MOOCs have at least started a new conversation, or perhaps revived an old one, about what an educational environment should look like, how a course should be constructed, what assessments should look like, and what student expectations should be. And for this, we–the 90% and the 10%–should thank them.

Back in early 2010 (wow, three years ago already), I was giving a plenary talk at a conference for Virginia K-12 teachers at a teaching and technology conference. There were about 400 people in the audience, and the basic gist of my talk was that technology continues to change every facet of life, and of course education should be no different. And in particular, technology allows/encourages us to use specific conceptual metaphors to understand information. Obviously, technology is not pedagogy, but at the same time technology-mediated pedagogies can be very powerful. At one point, I showed a clip from a much longer interview with Bill Gates and Steve Jobs. Gates says something about how finally–finally!–we are at the point where technology can really do something for education. After much optimism and many false starts, technology is now really a central part of new, emerging, powerful and effective pedagogies, and we have an “ecosystem” that supports this kind of work.

In the talk, I set up a great analogy between educational innovation, and the innovations of Apple’s iBooks platform (which had just been released when I gave this talk). The idea was that Apple was about to do for books what it did for music: radically change the way we conceive of the book, engage with the book, and think about the printed page. So I went through a very over-hyped introduction (see Slide 14 of the talk), and the showed a picture of the Apple iBooks icon…which looks exactly like a bookshelf. In the talk, I made a sort of exasperated and exaggerated gasp of chagrin that Apple, for all its amazing innovation and sleek design thinking, couldn’t come up with something better than a bookshelf. You can even see the grain of the wood. Incidentally, if you are curious about the future of the book, my colleague Michael Suarez is as smart as anybody in thinking about this.

Alas, this bookshelf serves a purpose: it is a (digital) skeuomorph. I was way ahead of the curve by talking about this in 2010. Since then, and in particular the latter part of 2012, skeuomorphic design has been much talked about in design circles. Why use skeuomorphs? The main reason is familiarity. When introducing new ideas or new technologies, we often need to anchor our understanding in comfortable conceptual metaphors; this is why we use terms like computer “desktop”, or Microsoft Word “document”, or web “page”. These things are not literally desktops or documents or pages, but that terminology immediately lets us know what functions those things serve.

Skeuomorphs serve a particular purpose that can be fruitfully considered in the diffusion of innovations framework championed by Rogers. In brief, the diffusion of innovations notion of technology or idea adoption within a community depends upon five basic issues:

relative advantage: compared to existing solutions, what relative advantage does this innovation provide?

compatibility: how consistent is this innovation with the cultural norms and values existing in the community?

complexity: what is the perceived difficulty in adopting and using the innovation?

trialability: how easy is it for people to try out and experiment with this innovation?

observability: how readily visible is the impact of this innovation?

Skeuomorphs, then, speak to compatibility, complexity, and trialability. The iBooks icon clearly signals to prospective users that: (i) the “books” contained within are exactly consistent with your understanding of what books are (high compatibility), (ii) if you know how to use a bookshelf, then you know how to use iBooks (low complexity), and (iii) using these books is as easy as walking over to a bookshelf, selecting a book, and starting to read (high trialability).

How does all this relate to education? We have learned through our HigherEd 2.0 project (the hard way, sometimes), that early adopters (say, the faculty deploying the innovations) have a larger appetite for technology innovations that non-early-adopters (say, students in the class). We simply cannot make too large a leap at a time with educational innovations, especially when technology is involved. With students, I believe the key is relative advantage and observability–students need to see clear and immediate evidence that the innovation supports their learning better than their previous approaches and strategies (relative advantage) and translates into higher achievement (i.e., higher grades) in the class (observability). Instructors simply cannot go too far of the regular track here. Instructors must build skeuomorphs into their teaching. How do you do that?

use thoughtful pedagogy: integrate the educational innovation into the class in a direct and well-explained way

make it easy for students to do: this relates to compatibility, complexity, and trialability and respects how students live and learn

model innovation usage: show students how to integrate innovative practices into their workflow by doing the same in class (and telling students what you are doing while you are doing it)

explain the scholarly basis behind the innovation: this is in my mind the most important; explaining to students what you are doing and why you are doing it (i.e., explaining your ideas about the relative advantage for them) goes a long way toward easing students’ concerns about adopting new approaches

Perhaps this is an emerging skeuomorphic pedagogy, necessitated by the rapid evolution of technology, but inhibited by the general, rather inertia-laden approaches to teaching in higher education. Early adopters and educational innovators will do well to consider skeuomorphic cues in their teaching so that their innovations can be greeted acceptingly by students and colleagues alike.

Nate Silver, the data-wonk-cum-blogger-cum-NYT-contributor-cum-statistical-demi-god-cum-media-darling of this week’s election…well, he got it right (compare his “forecast” with his “nowcast”). But what, exactly, did he get right, and how did he do it? The media and various pundits are enamored with Silver’s moxie and uncanny accuracy in predicting the election’s outcome. But it appears to me that the big winner of this story is cold, hard, sober data analytics. His blog is a playground of interesting, practical, well-founded analysis of data, data, and more data. This is big data, huge data, culled from multiple sources and giving specific state-by-state snapshots of the situation on the ground over time. This is no trivial task to synthesize all this information into a set of predictions.

Why are we so enamored when someone uses math productively? Think of it this way: in popular culture, when someone is good at math, people say: “wow, you must be really smart”. But when someone is good at, say, history, people say: “wow, you must really like history, and you probably studied a lot to get to be so knowledgeable about history.” It’s a bias, plain and simple, against the kind of basic quantitative literacy that will only become more important to this nation and the world over time. How can we evaluate election results, pollution data, SOL outcomes, or any other quantitative information without a basic foundation in, and respect for, general quantitative literacy?

To be exceptionally clear: I do not mean to imply that the polls are biased in Mr. Obama’s favor. But there is the chance that they could be biased in either direction. If they are biased in Mr. Obama’s favor, then Mr. Romney could still win; the race is close enough. If they are biased in Mr. Romney’s favor, then Mr. Obama will win by a wider-than-expected margin, but since Mr. Obama is the favorite anyway, this will not change who sleeps in the White House on Jan. 20.

My argument, rather, is this: we’ve about reached the point where if Mr. Romney wins, it can only be because the polls have been biased against him. Almost all of the chance that Mr. Romney has in the FiveThirtyEight forecast, about 16 percent to win the Electoral College, reflects this possibility.

Yes, of course: most of the arguments that the polls are necessarily biased against Mr. Romney reflect little more than wishful thinking.

It is both unfortunate and energizing to think that the general public (and the pundits in particular) might not fully appreciate how math works, how practical it can be, and why a systematic consideration of not just the mathematical operations, but also the quality of the input data, can lead to better predictions, or better policies, or better profits, or better quality of life. Unfortunate and frustrating, perhaps. But it’s also an important opportunity for us, as academics and people in the science/technology/mathematics literacy world (we are, after all, in higher education), one that should energize us with the challenge that lies ahead.

A Bold Proposal: Let’s develop a course on information literacy, required for every student at UVa, and continuously measure the outcomes and impact of that course on how students approach their academics and their life. An educated, global citizenry requires nothing less.

I’ve been thinking a lot lately about the skill set engineers graduating in the next few years will need to prepare them for a vibrant and productive career. I can think of very few skills that rival big data analytics in importance for the next generation of engineers. We continue to collect data on a vast scale every day. We need to look no further than data.gov, the repository of an astounding array of data from a huge swath of government agencies on subjects ranging from the crucial to the mundane. Data and analytics are everywhere, in every aspect of our lives; to wit:

commerce: the NYT ran a fascinating article on Target and its data collection and analytics efforts on its customers

sport: perhaps my favorite example, the Manchester City Football Club (England) has started an MCFC Analytics initiative, in which they release player performance data to the community, and the community is encouraged to analyze, graph, and otherwise break down the data “however you see fit”

Big data is here to stay, and everyone–engineer or not–needs basic literacy about how to access, analyze, interpret, and otherwise engage with data. So it is incumbent upon the faculty in higher education to give students opportunities, experiences, and training around this critical skill set.

Big data hits upon several of the key student learning outcomes that educators have wrestled with for many years:

an ability to pose research questions and gather sufficient resources/evidence to answer those questions

a general comfort with uncertainty, lack of complete information, poor signal-to-noise ratio, etc.

the ability to conduct data analysis, especially on large data sets, using modern computer tools

the ability to visualize data, and use graphics to tell a persuasive story about what the data means

These basic skills are part of the new literacy for all engaged citizens–not just scientists and engineers. And developing curricula around big data opens up some enticing new possibilities on student motivation and engagement: students can choose to ask and answer research questions about which they care, in topical areas that are meaningful to them. A student interested in environmental issues could analyze public data sets about pollution, air quality, or water quality. A student passionate about economics could look at unemployment rates, pay scales, or international trade. A student interested in energy could examine subsidies for green energy companies, consumption locally and worldwide, or performance data for various alternative energy technologies.

There’s much more to say here. But preliminarily the point is that when it comes to education and the future of an educated citizenry, basic literacy about how to understand data is more important than ever before.